Samples approximate second-order multivariate Gaussian knockoff variables for the original variables.
normalized n-by-p realization of the design matrix
either 'equi', 'sdp' or 'asdp' (default:'asdp') This will be computed according to 'method', if not supplied
whether to shrink the estimated covariance matrix (default: FALSE)
If the argument
shrink is set to TRUE, a James-Stein-type shrinkage estimator for
the covariance matrix is used instead of the traditional maximum-likelihood estimate. This option
requires the package
?corpcor::cov.shrink for more details.
Even if the argument
shrink is set to FALSE, in the case that the estimated covariance
matrix is not positive-definite, this function will apply some shrinkage.
To use SDP knockoffs, you must have a Python installation with
CVXPY. For more information, see the vignette on SDP knockoffs:
n-by-p matrix of knockoff variables
Candes et al., Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection, arXiv:1610.02351 (2016). https://statweb.stanford.edu/~candes/MF_Knockoffs/index.html
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